The present disclosure relates to the field of automated data processing. More specifically, the present disclosure is related to the automated analysis of call flow using discourse analysis. Discourse analysis applies the concept of dialog acts in order to identify the function of an utterance within a larger dialog. By identifying these functions, the functions can be sequentially mapped in order to analyze the flow of a dialog on a functional level. The present disclosure also relates to the field of automated data processing, and more specifically to the application of ontology programming to process and analyze communication data.
Analysis of a dialog on a functional level rather than a contextual level can then be leveraged to identify areas of a dialog for further contextual analysis, or to identify functional relationships between dialogs in a database or a corpus of dialogs to be analyzed as a group.
Analysis of a dialog on a functional level rather than a contextual level can then be leveraged to identify areas of a dialog for further contextual analysis, or to identify functional relationships between dialogs in a database or a corpus of dialogs to be analyzed as a group.
The term Dialog Act (DA) is used to denote some “function” of an utterance in a dialog. The goal behind the identification of dialog acts is to extract useful information from dialogs. The information is not at the level of syntax or semantics, but at a higher level related to the dialog structure and to the intentions of the speakers. Dialogue acts provide a useful way of characterizing dialogue behaviors in human-human dialogue. Identifying whether an utterance is a statement, question, greeting, and so forth is integral to effective automatic understanding of natural dialog.
An ontology is a formal representation of a set of concepts, and the relationships between those concepts in a defined domain. The ontology models the specific meanings of terms as they apply to that domain, and may be devised to incorporate one or several different spoken and/or written languages. Communication data may exist in the form of an audio recording, streaming audio, a transcription of spoken content, or any written correspondence or communication. In the context of a customer service interaction, the communication data may be a transcript between a customer service agent or interactive voice response (IVR) recording with a customer/caller. The interaction may be via phone, via email, via internet chat, via text messaging, etc. An ontology can be developed and applied across all types of communication data, for example, all types of customer interactions (which may include interactions in multiple languages), to develop a holistic tool for processing and interpreting such data.
Prior art data analysis systems and methods require manual data analysis to determine context and identify contextual patterns. For example, in a call center environment, calls are typically analyzed manually by a user listening to the call to determine the tone, context, and resulting success of the call. In one exemplary situation, prior art systems and software for analyzing call center data to assess the success of retention attempts by customer service representatives to retain customers who call intending to discontinue a product or service require a user to listen to calls, or portions of calls, to determine how the representative attempted to retain the customer and the success of that attempt. Currently, large companies have teams of people manually reviewing data to assess such call flows and success/failure rates.